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Improvement of signal-to-noise ratio by stochastic resonance in sigmoid function threshold systems, demonstrated using a CMOS inverter

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  • Ueda, Michihito

Abstract

Stochastic resonance (SR) has become a well-known phenomenon that can enhance weak periodic signals with the help of noise. SR is an interesting phenomenon when applied to signal processing. Although it has been proven that SR does not always improve the signal-to-noise ratio (SNR), in a strongly nonlinear system such as simple threshold system, SR does in fact improve SNR for noisy pulsed signals at appropriate noise strength. However, even in such cases, when noise is weak, the SNR is degraded. Since the noise strength cannot be known in advance, it is difficult to apply SR to real signal processing. In this paper, we focused on the shape of the threshold at which SR did not degrade the SNR when noise was weak. To achieve output change when noise was weak, we numerically analyzed a sigmoid function threshold system. When the slope around the threshold was appropriate, SNR did not degrade when noise was weak and instead was improved at suitable noise strength. We also demonstrated SNR improvement for noisy pulsed voltages using a CMOS inverter, a very common threshold device. The input–output property of a CMOS inverter resembles the sigmoid function. By inputting the noisy signal voltage to a CMOS inverter, we measured the input and output voltages and analyzed the SNRs. The results showed that SNR was effectively improved over a wide range of noise strengths.

Suggested Citation

  • Ueda, Michihito, 2010. "Improvement of signal-to-noise ratio by stochastic resonance in sigmoid function threshold systems, demonstrated using a CMOS inverter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 1978-1985.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:10:p:1978-1985
    DOI: 10.1016/j.physa.2010.01.035
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    References listed on IDEAS

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    1. Ueda, Michihito & Ueda, Masahiro & Takagi, Hiroaki & Sato, Masayuki J. & Yanagida, Toshio & Yamashita, Ichiro & Setsune, Kentaro, 2008. "Biologically-inspired stochastic vector matching for noise-robust information processing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4475-4481.
    2. Munakata, Toyonori & Hada, Takahiro & Ueda, Michihito, 2007. "Self-tuning and stochastic resonance in a simple threshold system—a filter theory approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 492-498.
    3. Robert L. Badzey & Pritiraj Mohanty, 2005. "Coherent signal amplification in bistable nanomechanical oscillators by stochastic resonance," Nature, Nature, vol. 437(7061), pages 995-998, October.
    4. M. I. Dykman & P. V. E. McClintock, 1998. "What can stochastic resonance do?," Nature, Nature, vol. 391(6665), pages 344-344, January.
    5. David F. Russell & Lon A. Wilkens & Frank Moss, 1999. "Use of behavioural stochastic resonance by paddle fish for feeding," Nature, Nature, vol. 402(6759), pages 291-294, November.
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    Cited by:

    1. Duan, Fabing & Chapeau-Blondeau, François & Abbott, Derek, 2011. "Neural signal transduction aided by noise in multisynaptic excitatory and inhibitory pathways with saturation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2855-2862.

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